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GEMuseXMLReader

     Python class for reading GE® MUSE® XML files. Returns a header with most of the file configurations and the lead's data is available as a Numpy array or a Pandas data frame.

Dependencies

     This reader needs the following libraries to parse the XML files:

  • xmltodict (To read and create a dictionary from the XML file)
  • numpy (To save the data from the leads into an array)
  • pandas (To save the data from the leads into a structured data frame)
  • xlwt (To save the lead data in a Excel file)

Usage

     The GEMuseXMLReader can be used in two different approaches:

  • as a converter
  • as a python class for accessing the data

     Converting XML file

     The GEMuseXMLReader can be called in the command line to convert the XML into a CSV, JSON, Excel, Numpy object, or all. Paired with the converted file is a JSON with the header information.

python3 GEMuseXMLReader.py 'filename.XML' [arguments]

               Arguments

  • [-csv [CSV]] - Convert the XML to a CSV. Output filename is optional.
  • [-pcsv [PCSV]] - Convert the XML to a CSV (Pandas formated). Output filename is optional.
  • [-ops [OPS]] - Convert the XML to a txt compatible with PLUX's opensignals. Output filename is optional.
  • [-x [EXCEL]] - Convert the XML to a Excel. Output filename is optional.
  • [-np [NUMPY]] - Convert the XML to a Numpy. Output filename is optional.
  • [-json [JSON]] - Convert the XML to a JSON. Output filename is optional.
  • [-all [ALL]] - Convert the XML to a CSV, Excel, Numpy and JSON. Output filename is optional.

     Python class

     The GEMuseXMLReader can be also be imported by another python script and used to converted the XML files, providing the data in either a Numpy array or a Panda data frame.

import GEMuseXMLReader

GEMuseData = GEMuseXMLReader('filename.XML')

GEMuseData.header ## Header containing the patient, device and acquisition session parameters

GEMuseData.dataObject ## Dictionary containing the data separated by lead

GEMuseData.dataFrame ## Panda's data frame containg the acquisition data

GEMuseData.dataArray ## Numpy matrix containing the acquisition data

Header

     The header is structured as follows:

  • Header

    • PatientInfo

      • Given_Name
      • Family_Name
      • ID
      • Gender
      • Race
      • Pacemaker

    • DeviceInfo

      • DeviceModel
      • DeviceName
      • DeviceSerial

    • AcquisitionInfo

      • AcquisitionDate
      • LeadAmplitudeUnitsPerBit
      • LeadAmplitudeUnits
      • Resolution
      • Filters
        • HighPass
        • LowPass
        • Algorithms*
          • Name
          • Purpose
      • SampleRate
      • LeadsInformation
        • LeadsNames
        • LeadsLabels
        • NumberOfSamples
      • HeaderString

  • Could be more than one.